J
James J. Little
Researcher at University of British Columbia
Publications - 216
Citations - 13732
James J. Little is an academic researcher from University of British Columbia. The author has contributed to research in topics: Mobile robot & Optical flow. The author has an hindex of 51, co-authored 211 publications receiving 12423 citations. Previous affiliations of James J. Little include Simon Fraser University & Massachusetts Institute of Technology.
Papers
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Book ChapterDOI
A Boosted Particle Filter: Multitarget Detection and Tracking
TL;DR: This work introduces a vision system that is capable of learning, detecting and tracking the objects of interest, and interleaving Adaboost with mixture particle filters, a simple, yet powerful and fully automatic multiple object tracking system.
Proceedings ArticleDOI
A Simple Yet Effective Baseline for 3d Human Pose Estimation
TL;DR: In this paper, a relatively simple deep feed-forward network was proposed to estimate 3D human pose from 2D joint locations with a remarkably low error rate, achieving state-of-the-art results on Human3.6M.
Journal ArticleDOI
Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks
TL;DR: A vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments to localize itself accurately and build a map of the environment.
Proceedings ArticleDOI
Vision-based mobile robot localization and mapping using scale-invariant features
TL;DR: A vision-based mobile robot localization and mapping algorithm is described which uses scale-invariant image features as landmarks in unmodified dynamic environments which are localized and robot ego-motion is estimated by matching them, taking into account the feature viewpoint variation.
Journal ArticleDOI
Vision-based global localization and mapping for mobile robots
TL;DR: Experiments show that global localization can be achieved accurately using the scale-invariant landmarks, and the approach of pairwise submap alignment with backward correction in a consistent manner produces a better global 3-D map.